Skin Disease Detection System

Given the fact that some of these diseases are difficult to establish diagnosis, including psoriasis, melanoma, eczema, seborrheic keratoses, and basal cell carcinoma, demand for better and more accurate diagnostic methods is always real. This paper will develop the application of CNN models based on the images sourced from the DermNet dataset to automatically classify the above five dermatological conditions. In addition, this dataset was processed with image scaling, normalization, and augmentation to ensure consistency and robustness of the method. The dimensionality was determined by feature extraction with the design of multiple convolutional layers of the CNN architecture. The model compilation was made by setting sparse categorical cross-entropy as the loss function combined with the Adam optimizer. The learned hyperparameters are the changes in the learning rate, batch size, and dropout rates. The metrics with Fl-score, precision, and recall set the means for judging the effectiveness of the model as all classes were said to score high accuracy for classification. This research, therefore, only serves to establish that AI models are relevant in the field of dermatology, though, there should be more research and clinical validation indeed.